Energy efficient resource management for real-time IoT applications

The Internet of Things (IoT) has a large and rapidly expanding number of deployed devices, which leads to a significant global energy consumption footprint. Diverse IoT use cases, including smart cities, smart grids, Industry 5.0, eHealth, and autonomous vehicles, are contributing to this increase in energy consumption. Optimising energy utilisation is crucial to sustaining the exponential growth of IoT applications, which demand stringent delays and latencies measured in milliseconds and microseconds. There are additional complexities with the emergence of edge, fog, and cloud computing and the need to manage the energy consumption at all the layers. In this paper, mechanisms that can be used to minimise energy consumption within an edge-fog-cloud IoT architecture for real-time IoT applications are being proposed. We investigate mechanisms for optimal node selection, primarily focusing on minimising energy usage while adhering to the Quality of Service (QoS) requirements of various IoT requests. The mechanisms include genetic, modified genetic, and delay-aware algorithms tailored explicitly for real-time IoT applications. We evaluated the proposed mechanisms using a simulation of diverse network scenarios. The results presented in the paper provide insight into balancing processing time and energy efficiency, which are critical considerations in sustainably developing IoT applications in an edge-fog-cloud IoT architecture.